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Join us for an eye-opening exploration of how virtual patient data is revolutionising paediatric clinical trials. In our upcoming episode, we delve into groundbreaking research that addresses one of healthcare's most pressing challenges: the power crisis plaguing paediatric randomised controlled trials. Discover how digital twins, synthetic patient data, and in silico trials are offering unprecedented solutions to reduce children's exposure to unproven therapies whilst accelerating drug approvals. We'll examine the transformative potential of these technologies in creating personalised treatment options at significantly lower costs, ultimately leading to faster clinical implementation of life-saving interventions. However, innovation comes with responsibility. Our discussion critically evaluates the ethical and regulatory frameworks necessary to ensure safe, sustainable adoption of virtual patient data in paediatric medicine. Join our interactive webinar to engage directly with experts and explore how these digital innovations are shaping the future of children's healthcare. Source: Pammi M, Shah PS, Yang LK, Hagan J, Aghaeepour N, Neu J. Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials? Lancet Digit Health. 2025      

This episode explores groundbreaking research from Boston Children's Hospital and Harvard Medical School that demonstrates how multiphysiologic state computational fluid dynamics (CFD) modelling is transforming surgical planning for children with complex congenital heart conditions. We delve into the innovative 3D virtual surgery workflow developed for patients with single ventricle physiology and interrupted inferior vena cava - a particularly challenging combination that historically carries high risks of life-threatening complications. The research team's approach utilises advanced CFD analysis across multiple physiological states to predict and optimise hepatic venous flow distribution, preventing the formation of pulmonary arteriovenous malformations that can prove fatal in these vulnerable young patients. The episode examines how this in silico methodology successfully translates virtual surgical planning into real-world clinical outcomes, validated through post-operative MRI imaging. We discuss the broader implications for personalised paediatric medicine, the potential for reducing surgical revisions, and how computational modelling is enabling surgeons to achieve balanced blood flow patterns that were previously difficult to predict using traditional planning methods. This case study exemplifies the transformative potential of digital health technologies in paediatric care, showcasing how sophisticated computational tools can directly improve surgical outcomes and quality of life for children with complex cardiac conditions. Source: Hoganson DM, Govindarajan V, Schulz NE, Eickhoff ER, Breitbart RE, Marx GR, Del Nido PJ, Hammer PE. Multiphysiologic State Computational Fluid Dynamics Modeling for Planning Fontan With Interrupted Inferior Vena Cava. JACC Adv. 2024 Jun 13;3(7):101057.      

Are you prepared for the seismic shift transforming UK life sciences careers? This latest "In Silico Trials, Real Impacts!" episode reveals how AI and in silico technologies are creating unprecedented employment opportunities across health tech sectors.  Discover the essential skills driving this revolution, from computational biology to digital therapeutics, and why lifelong learning has become non-negotiable. We examine the geographical spread of emerging roles and explore how international talent maintains Britain's competitive advantage in global markets. What does this transformation mean for traditional biologists, chemists, and clinicians as digital tools reshape their practice? How are government initiatives preparing the next generation for these evolving demands?  Uncover the real-world implications for patient care and economic growth in this data-driven analysis of our industry's future. Sources: Lightcast (2024), The UK Skills Revolution: Building a Data-Driven Skills System in an Era of Disruption https://lightcast.io/resources/research/uk-skills-revolution-25 Lightcast, ABHI, ABPI, and BIA (2025) Life Sciences 2035: Developing the Skills for Future Growth https://www.abpi.org.uk/publications/life-sciences-2035-developing-the-skills-for-future-growth-main-report/      

Are Randomised Controlled Trials (RCTs) always the definitive 'gold standard' in research? In the latest episode of In Silico Trials, Real Impacts!, we explore the nuanced landscape of RCTs, questioning their universal applicability and examining the complexities they often obscure. While RCTs are celebrated for their rigour, this discussion delves into their limitations, particularly when findings are applied across diverse contexts. Drawing on seminal work by Deaton and Cartwright (2018), this episode highlights the critical role that RCTs play in the medical and social sciences, while underscoring the importance of complementing them with observational studies and theoretical frameworks. Ethical considerations and the need for a more integrated approach to research take centre stage, offering a fresh perspective on evidence generation. Whether you're a researcher, healthcare professional, or simply curious about scientific methodologies, this episode provides thought-provoking insights into the evolving landscape of research. Source: Deaton A, Cartwright N. Understanding and misunderstanding randomized controlled trials. Soc Sci Med. 2018 Aug;210:2-21.      

Is simplicity always the smartest choice in science? Or could complexity be the key to groundbreaking discoveries? This episode explores the evolving role of simplicity and complexity in scientific modelling, questioning the age-old principle of Occam's razor in light of modern advancements: Plurality should not be posited without necessity.  Discover how digital approaches, from computational modelling to simulated clinical trials, are reshaping medical product development, making healthcare safer, more efficient, and unexpectedly precise. As we journey through recent research and breakthroughs, you'll gain insights into the tension between simple and complex scientific models, and how they impact the healthcare landscape. Whether you're a researcher, regulator, patient representative, or just curious about the future of medicine, this episode unpacks the concept of parsimony and its implications, especially as technology progresses. Learn about the balance between simplicity and complexity and how they complement each other as tools in science.   Source: Dubova M, Chandramouli S, Gigerenzer G, Grünwald P, Holmes W, Lombrozo T, Marelli M, Musslick S, Nicenboim B, Ross LN, Shiffrin R, White M, Wagenmakers EJ, Bürkner PC, Sloman SJ. Is Ockham's razor losing its edge? New perspectives on the principle of model parsimony. Proc Natl Acad Sci U S A. 2025 Feb 4;122(5):e2401230121.            

Could computer simulations redefine diabetes care? In this episode, we focus on a real medical product and the role of in silico trials in the transition from the Medtronic Guardian Sensor 3 to the Guardian 4 sensor, central to the Medtronic 780G pump system, which aims to simplify diabetes management by eliminating the need for fingerstick calibrations. Discover how computer simulations, clinical studies, and real-world data converge to evaluate this significant shift, revealing slight shifts in glucose metrics but proving the reliability of in silico methods in predicting real outcomes. Join us to uncover the potential of digital modeling to transform healthcare, making it more precise while retaining safety, and contemplate how these advances might accelerate personalized medicine delivery, bypassing some traditional trial limitations. Source: Grosman B, Parikh N, Roy A, Lintereur L, Vigersky R, Cohen O, Rhinehart A. In Silico Evaluation of the Medtronic 780G System While Using the GS3 and Its Calibration-Free Successor, the G4S Sensor. Ann Biomed Eng. 2023 Jan;51(1):211-224.      

What is Evidence-Based Medicine Plus? This podcast episode explores the transformative power of computational modelling and simulated clinical trials in medical innovation. Discover the significance of evidence-based medicine (EBM) and evidential pluralism, emphasising the role of mechanistic evidence alongside traditional studies. Join us as we delve into compelling examples like mobile phone radiation and ACE inhibitors, highlighting how these insights enhance the external validity of treatments. Perfect for researchers and regulators alike, or anyone interested in the evolving landscape of medicine. Sources: Russo F, Williamson J. Epistemic causality and evidence-based medicine. Hist Philos Life Sci. 2011;33(4):563-81. Parkkinen, V.-P., Wallmann, C., Wilde, M., Clarke, B., Illari, P., Kelly, M. P., Norell, C., Russo, F., Shaw, B., & Williamson, J. (Eds.). (2023). Evaluating Evidence of Mechanisms in Medicine: Principles and Procedures. Oxford University Press.      

How do philosophers of science think about model evaluation? Dive deep into the crucial process of model evaluation, a fundamental step in digital science that determines the effectiveness and precision of computational models. This episode explores three primary perspectives on model quality: the mirror view, relevant similarity, and fitness for purpose. Learn about the complexities and challenges involved in evaluating models, from data limitations to the intricate balance between accuracy and usability. Whether you're a researcher, regulator, or a curious listener, discover how these cutting-edge methodologies are reshaping healthcare, improving safety, efficiency, and equitability, one simulated trial at a time. Join us as we delve into the philosophy behind model evaluation and unveil the real-world impact of digital innovation. Parker WS. Model Evaluation: An Adequacy-for-Purpose View. Philos Sci. 2020;87(3):457-477. Bokulich A, Parker W. Data models, representation and adequacy-for-purpose. Eur J Philos Sci. 2021;11(31).         

Have you ever wondered what makes a digital twin truly "twin-like" and not just another computer model? This fascinating question lies at the heart of modern healthcare innovation, where virtual copies of real-world systems are revolutionising how we approach medical research and patient care. Our latest episode delves into groundbreaking research that examines the philosophical underpinnings of digital twins, exploring what sets them apart from conventional computational models. We unpack how digital twins are transforming healthcare through their unique ability to capture complex, emergent behaviours in ways traditional models cannot. From enabling safer drug development through in silico trials to advancing personalised medicine, these sophisticated virtual representations are bridging the gap between computational simulation and real-world applications. Our discussion reveals why digital twins represent more than just technological advancement - they embody a fundamental shift in how we understand and interact with healthcare systems, promising more precise, efficient, and safer medical innovations for the future. Reference: Wagg DJ, Burr C, Shepherd J, Xuereb Conti Z, Enzer M, Niederer S. The philosophical foundations of digital twinning. Data-Centric Engineering. 2025;6:e12. doi:10.1017/dce.2025.4      

Are computational models ready to replace traditional clinical trials? This episode delves into the fascinating world of in silico trials and their growing role in regulatory evaluation of biomedical products. We explore a methodological framework based on the ASME VV-40-2018 standard that establishes credibility through verification, validation, and uncertainty quantification. From defining contextual use to conducting thorough risk analysis, we examine how these principles apply across statistical, machine learning, Bayesian, and agent-based models. We compare regulatory approaches between different authorities and make the case for wider adoption of credibility assessment standards to ensure reliable virtual evidence. Join us as we navigate the cutting edge of computational modelling in healthcare regulation.   Viceconti M, Pappalardo F, Rodriguez B, Horner M, Bischoff J, Musuamba Tshinanu F. In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods. 2021 Jan;185:120-127. 

Is the healthcare sector prepared to harness synthetic data's transformative potential whilst navigating its hidden pitfalls? This thought-provoking episode delves into the murky waters of synthetic data governance in medical research, examining how generative AI is reshaping our approach to patient information. As algorithms increasingly create convincing facsimiles of real clinical data, we explore the tantalising benefits—enhanced privacy protection, expanded research capabilities, and accelerated innovation cycles—alongside the concerning governance vacuum that threatens to undermine these advances. Our discussion unpacks why the absence of standardised creation and evaluation frameworks is stalling widespread adoption, despite synthetic data's promise. We make the case for urgent development of robust data standards, ethical guidelines and cross-disciplinary collaboration to ensure this powerful tool serves healthcare's highest goals. Join us as we navigate the complex terrain where technological innovation meets patient protection, and discover why establishing clear governance frameworks today is essential for tomorrow's medical breakthroughs.   Boraschi D, van der Schaar M, Costa A, Milne R. Governing synthetic data in medical research: the time is now. Lancet Digit Health. 2025 Feb 20:S2589-7500(25)00011-1. 

Are you curious about how computer simulations are changing the way we evaluate medical devices? In this episode, we delve into the fascinating world of in silico clinical trials (ISCTs), a cutting-edge approach that uses computational models to assess the safety and effectiveness of medical interventions. We explore a recent paper that highlights the unique challenges of ensuring the credibility of ISCT results. The authors present a comprehensive workflow for assessing this credibility, drawing on various methodologies and frameworks, including those from the FDA. By reviewing a range of ISCT studies, we discuss innovative patient model generation techniques, the integration of clinical outcomes, and how these virtual trials can complement traditional clinical studies. Join us as we unpack the potential of ISCTs to revolutionize medical device evaluation, making it more cost-effective, time-efficient, and ethically sound. Source: Bodner J, Kaul V. A framework for in silico clinical trials for medical devices using concepts from model verification, validation, and uncertainty quantification. J Verif Valid Uncertain Quantif. 2022;7(2):021001.                           

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝘁𝗿𝗶𝗮𝗹 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿? This episode explores the fascinating world of virtual patient models and stochastic engineering, unveiling how digital simulations are revolutionising healthcare clinical trials. Discover how simulated trials using sophisticated virtual patient models—complete with variations in age, body size, and disease progression—are introducing groundbreaking innovations in patient care. We delve into stochastic engineering models that cleverly integrate uncertainty into device design, offering more precise and realistic predictions of clinical outcomes. Learn about the power prior method—a dynamic approach to combining digital and real-world evidence in clinical trials, enhancing efficiency and accelerating device delivery to patients whilst maintaining rigorous safety and accuracy standards. Join us for an illuminating discussion at the cutting edge of healthcare innovation, where virtual modelling meets real-world patient impact.   Haddad T, Himes A, Thompson L, Irony T, Nair R; MDIC Computer Modeling and Simulation Working Group Participants. Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. J Biopharm Stat. 2017;27(6):1089-1103. 

How are the CHEERS-AI guidelines transforming economic evaluations of healthcare AI? In our latest podcast episode, we delve into how the Consolidated Health Economic Evaluation Reporting Standards for AI (CHEERS-AI) are revolutionizing the economic assessment landscape for artificial intelligence in healthcare. Discover why transparency and consistency are crucial, and how this comprehensive framework helps determine the true financial value of AI innovations in the medical field. This episode navigates the intricate challenges of healthcare AI integration, including uncovering hidden costs and addressing the "black box" problem—where understanding AI's decision-making processes remains difficult yet essential for proper evaluation and implementation. Join our conversation as we explore the broader ethical considerations and societal impacts of healthcare AI, emphasizing the importance of responsible innovation. Whether you're a health economist, researcher, or simply interested in AI's transformative potential in healthcare, this episode offers valuable perspectives on balancing innovation with responsibility to ensure AI advancements benefit both patients and healthcare systems alike. Elvidge J, Hawksworth C, Avşar TS, Zemplenyi A, Chalkidou A, Petrou S, Petykó Z, Srivastava D, Chandra G, Delaye J, Denniston A, Gomes M, Knies S, Nousios P, Siirtola P, Wang J, Dawoud D; CHEERS-AI Steering Group. Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI). Value Health. 2024 Sep;27(9):1196-1205. 

Will the MHRA's New Data Strategy Transform Healthcare Through Computational Innovation? In this episode, we explore the MHRA's groundbreaking data strategy for 2024-2027, a visionary plan heralding a fourth industrial revolution in healthcare. From supporting data-driven innovation to harnessing AI and real-world evidence, we dive deep into the strategic themes aimed at revolutionizing medical product development and regulation. Learn how AI is set to change the game, enabling quicker development of treatments tailored to individual needs and monitoring safety like never before. We also address the challenges of data fragmentation, quality issues, and privacy concerns, while highlighting ambitious initiatives like the Centers of Excellence in Regulatory Science and Innovation. Tune in to uncover how this strategy could position the UK as a leader in data-driven healthcare, inspiring global shifts towards personalized and equitable treatment for all. Source: MHRA (2024) MHRA Data Strategy 2024 - 2024. www.gov.uk/government/publications/mhra-data-strategy-2024-2027